1 Mount Hood Environmental, PO Box 1303, Challis, Idaho, 83226, USA
2 Mount Hood Environmental, 39085 Pioneer Boulevard #100 Mezzanine, Sandy, Oregon, 97055, USA
3 Mount Hood Environmental, PO Box 4282, McCall, Idaho, 83638, USA
✉ Correspondence: Bryce N. Oldemeyer <Bryce.Oldemeyer@mounthoodenvironmental.com>, Mark Roes <mark.roes@mthoodenvironmental.com>
Quantile random forest (QRF) models have become an increasingly popular tool for quantifying freshwater habitat carrying capacity due to their flexible framework that avoid common pitfalls associated with noisy data, correlated variables, and non-linear relationships. Recently, three QRF models were developed using large fish-habitat datasets (CHaMP dataset) within the Columbia River Basin to estimate carrying capacity for ESA-listed populations of Chinook salmon and steelhead during three critical life-stages (juvenile summer parr, juvenile winter presmolt, and adult redds). The covariates included in those models were selected from >100 potential covariates and chosen for their high predictive power to estimate capacity across the Columbia River Basin (cite IRA, See et al 2021, or another document outlining the original QRf selection process). However, a subset of the covariates included the QRF models are not useful for restoration project monitoring, are not informative for describing target conditions for restoration design due to the covariates inability to be manipulated by project actions, and are difficult to replicate or measure using streamlined fish habitat protocols (DASH - Carmichael et al. 2019). To increase the utility of the QRF model for project monitoring, project design, and future data collection efforts, we explored including alternative covariates in the QRF models that: 1) maintained high predictive power, 2) were informative for restoration efforts and monitoring, 3) could be calculated from DASH surveys, 4) were not missing an overabundance of data in the fish-habitat dataset, and 5) were not highly correlated with other covariates in the models (avoid overfitting the models). Additionally, we wanted to test the assumption made during the development of the original QRF model that a single model was appropriate for both Chinook salmon and steelhead during each of the three life stages.
Similarly, a random forest (RF) model has been used to predict habitat capacity estimates across larger spatial scales where CHaMP and/or DASH data aren’t available (cite IRA). We revisited the globally available attributes (GAAs) included in the original RF extrapolation model and made minor modifications to the extrapolation model that: 1) maintained covariates with high predictive power and 2) included covariates that better aligned with the revised QRF model covariates. To compare relative performance between the original and modified RF extrapolation models, we evaluated watershed carrying capacity estimates produced by the two models for eight watersheds located within the Upper Salmon River basin.
Through this process, we successfuly developed three modified QRF model that were more informative for restoration design and monitoring, included covariates that could be calculated using newly developed stream habitat protocols, and maintained a similar level of predictive power as the original QRF habitat capacity models. Below is a brief document outlining these efforts.
Potential habitat covariates for the QRF models were generated from the CHaMP dataset or obtained from other sources (e.g. NorWest stream temperature data). In total, 129 covariates were included in the selection process. Covariates were aggregated into eleven metric categories and 1-4 metrics were chosen from each category based on the rubric below.
What was the strength between the covariate and the response variable (based on MIC score)?
Could the covariate be calculated using DASH data?
Was the covariate informative for restoration efforts?
How much data were missing and/or the amount of “0”s for the covariate in the fish-habitat dataset?
How correlated was the covariate with other covariates within the same metric category, particularly with covariates with higher MIC scores?
An oversimplified example of how a theoretical covariate might be selected for a model is described as follows.
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In the original QRF model, discharge was likely selected as a covariate because it had a high MIC score and it made biological sense (i.e. discharge is a significant factor impacting fish habitat use and, presumably, habitat carrying capacity). Unfortunately, discharge isn’t that informative for restoration efforts because most restoration actions can’t create water. Discharge, like many habitat covariates, is highly correlated to other habitat covariates but these other covariates may have been left out of the original QRF model for any number of reasons (highly correlated with other covariates already in the model, excluded to avoid overfitting the model, etc.). Using the rubric, it is observed that average thalweg depth has a MIC score that is nearly as high as discharge, it is informative for restoration efforts, it can be calculated with DASH, and the two covariates are highly correlated (the high correlation is likely why average thalweg depth was left out of the original QRF model). Based on all the information above, mean thalweg depth would be substituted for discharge in the model. This process would be repeated for all the remaining covariates for that model.
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Last, the covariate selection process was done independently for both species for all three life stages to test the assumption made during the original QRF model development that the top covariates for the three life stages were the same between species.
There were 12-14 covariates selected for each of the six models. While the relative importance of the final covariates in the three life stage models differed between species, the final covariates themselves were nearly identical. (Figure 3.1 , Figure 3.2, and Figure 3.3 ). Because of this, we consolidated the species-specific models into a single winter juvenile, summer juvenile, and redd models to be used for both species (Table 3.1).
Figure 3.1: Relative importance plots for covariates included in the juvenile summer QRF models
Figure 3.2: Relative importance plots for covariates included in the juvenile winter QRF models
Figure 3.3: Relative importance plots for covariates included in the QRF redds models
| Name | Metric Category | Juv Sum Chnk | Juv Sum Sthd | Juv Win Chnk | Juv Win Sthd | Redds Chnk | Redds Sthd | Description |
|---|---|---|---|---|---|---|---|---|
| Channel Unit Frequency | ChannelUnit | 5 | 9 | 5 | 3 | 1 | 1 | Number of channel units per 100 meters. |
| Fast NonTurbulent Frequency | ChannelUnit | 6 | 13 | – | – | 13 | 4 | Number of Fast Water Non-Turbulent channel units per 100 meters. |
| Sinuosity | Complexity | 13 | 7 | 10 | 10 | 10 | 12 | Ratio of the thalweg length to the straight line distance between the start and end points of the thalweg. |
| Wetted Channel Braidedness | Complexity | 14 | 14 | 13 | 13 | – | – | Ratio of the total length of the wetted mainstem channel plus side channels and the length of the mainstem channel. |
| Fish Cover: Some Cover | Cover | 8 | 4 | 8 | 8 | 9 | 3 | Percent of wetted area with some form of fish cover |
| Large Wood Density | Cover | – | – | 4 | 5 | – | – | Large Wood per sq meter |
| Residual Depth | Size | – | – | 2 | 2 | – | – | Average residual depth of the channel unit. |
| Average Thalweg Depth | Size | 1 | 3 | – | – | 2 | 2 | Average Thalweg Depth, meters |
| Thalweg Exit Depth Avg | Size | – | – | 6 | 7 | – | – | Depth of the thalweg at the downstream edge of the channel unit. |
| Gradient | Size | 3 | 2 | 7 | 1 | 4 | 6 | Site water surface gradient is calculated as the difference between the top of site (upstream) and bottom of site (downstream) water surface elevations divided by thalweg length. |
| Residual Pool Depth | Size | 12 | 10 | – | – | 11 | 5 | The average difference between the maximum depth and downstream end depth of all Slow Water/Pool channel units. |
| Discharge | Size | – | – | 3 | 4 | – | – | The sum of station discharge across all stations. Station discharge is calculated as depth x velocity x station increment for all stations except first and last. Station discharge for first and last station is 0.5 x station width x depth x velocity. |
| Substrate Est: Boulders | Substrate | 10 | 12 | – | – | 8 | 11 | Percent of boulders (256-4000 mm) within the wetted site area. |
| Substrate Est: Cobble and Boulder | Substrate | – | – | 11 | 11 | – | – | Total cobble plus boulder percentage |
| Substrate Est: Cobbles | Substrate | 11 | 6 | – | – | 5 | 8 | Percent of cobbles (64-256 mm) within the wetted site area. |
| Substrate Est: Coarse and Fine Gravel | Substrate | 7 | 8 | 12 | 12 | 7 | 13 | Percent of coarse and fine gravel (2-64 mm) within the wetted site area. |
| Substrate Est: Sand and Fines | Substrate | 9 | 5 | 9 | 9 | 6 | 7 | Percent of sand and fine sediment (0.01-2 mm) within the wetted site area. |
| Avg. August Temperature | Temperature | 2 | 1 | – | – | 3 | 10 | Average predicted daily August temperature from NorWest, averaged across the years 2002-2011. |
| Elevation | Temperature | – | – | 1 | 6 | – | – | Elevation, meters |
| Large Wood Frequency: Wetted | Wood | 4 | 11 | – | – | 12 | 9 | Number of large wood pieces per 100 meters within the wetted channel. |
The spatial extent of QRF capacity predictions was/is limited to reaches with high-resolution habitat data (i.e. CHaMP or DASH data), so an extrapolation model was developed to estimate habitat capacity for the Columbia River Basin using “globally available attributes” (GAAs) obtained from a continuous, linear, stream network created by Morgan Bond and Tyler Nodine based on the National Hydrography Dataset High Resolution 1:24,000. Using the GAAs from the linear stream network and a random forest model structure, capacity estimates at the 200 meter reach scale for the entire Columbia River Basin. Consistent with the QRF model, the extrapolation model makes no assumptions about the direction and distribution of effects of predictors, and constrains density estimates within the range of predictions produced by the QRF model. However, random forest methods do not account for variable strata weights across the CHaMP dataset, a source of potential bias that could be alleviated through the collection of additional paired fish and habitat data.
Extrapolation model covariates were selected from the list of GAAs and examined for inclusion by examining relative importance and partial dependence plots and correlation between covariates. We used the covariates included in the previous extrapolation as a starting point for selection. This resulted in the replacement of regime (an indicator of dominant precipitation type) for elevation and the removal of relative slope, which we found was redundant with gradient. Model results indicated that elevation was consistently one of the most important predictors in the model. This is particularly true for the Chinook parr summer model where capacity predictions were primarily driven by elevation.
| Metric | Decription |
|---|---|
| Gradient % | Stream gradient (%). |
| Sinuosity | Reach sinuosity. 1 = straight, 1 < sinuous. |
| Alpine accumulation | Number of upstream cells in alpine terrain. |
| Fines accumulation | Number of upstream cells in fine grain lithologies. |
| Flow accumulation | Number of upstream DEM cells flowing into reach. |
| Gravel accumulation | Number of upstream cells in gravel producing lithologies. |
| Precipitation accumulation | Number of upstream cells weighted by average annual precipitation. |
| Floodplain width | Current unmodified floodplain width. |
| Avg Aug stream temperature | Historical composite scenario representing 10 year average August mean stream temperatures for 2002-2011 (Isaak et al. 2017). |
| Disturbance PCA 1 | Disturbance Classification PCA 1 Score (Whittier et al. 2011). |
| Natural PCA 1 | Natural Classification PCA 1 Score (Whittier et al. 2011). |
| Natural PCA 2 | Natural Classification PCA 2 Score (Whittier et al. 2011). |
| Elevation | Elevation at downstream end of reach |
Figure 4.1: Extrapolations of habitat capacity for Chinook salmon, by life-stage, for the eight watersheds within the Upper Salmon River Basin using the modified models.
Figure 4.2: Extrapolations of habitat capacity for steelhead, by life-stage, for the eight watersheds within the Upper Salmon River Basin using the modified models.
| Watershed | Juv summer capacity/km | Summer SE/km | Juv winter capacity/km | Winter SE/km | Redd capacity/km | Redd SE/km |
|---|---|---|---|---|---|---|
| EF Salmon | 12,335 | 1,452.9 | 885 | 210.5 | 3 | 0.1 |
| Lemhi | 5,766 | 459.4 | 1,038 | 112.6 | 3 | 0.1 |
| NF Salmon | 6,504 | 961.3 | 1,351 | 199.5 | 3 | 0.1 |
| Pahsimeroi | 5,146 | 357.3 | 1,689 | 189.8 | 3 | 0.1 |
| Panther Cr | 8,544 | 829.3 | 1,410 | 156.2 | 3 | 0.1 |
| Upper Salmon | 17,082 | 1,823.5 | 862 | 235.9 | 3 | 0.1 |
| Valley Cr | 15,833 | 1,726.0 | 961 | 270.8 | 3 | 0.2 |
| Yankee Fork | 14,967 | 1,916.6 | 833 | 200.9 | 3 | 0.2 |
| Watershed | Juv summer capacity | Summer SE | Juv winter capacity | Winter SE | Redd capacity | Redd SE |
|---|---|---|---|---|---|---|
| EF Salmon | 252,597 | 15,520.5 | 337,682 | 36,795 | 413 | 24 |
| Lemhi | 310,577 | 9,082.3 | 363,898 | 27,441 | 441 | 18 |
| NF Salmon | 242,471 | 18,381.8 | 313,118 | 27,955 | 323 | 22 |
| Pahsimeroi | 159,705 | 6,225.1 | 205,921 | 13,951 | 198 | 8 |
| Panther Cr | 268,476 | 13,598.0 | 339,671 | 19,946 | 317 | 15 |
| Upper Salmon | 243,548 | 14,843.6 | 310,879 | 39,013 | 452 | 32 |
| Valley Cr | 176,048 | 10,707.6 | 288,579 | 31,329 | 365 | 26 |
| Yankee Fork | 197,926 | 12,378.9 | 341,310 | 38,555 | 449 | 36 |
| Watershed | Juv summer capacity/km | Summer SE/km | Juv winter capacity/km | Winter SE/km | Redd capacity/km | Redd SE/km |
|---|---|---|---|---|---|---|
| EF Salmon | 1,525 | 93.7 | 2,039 | 222.2 | 2 | 0.1 |
| Lemhi | 1,774 | 51.9 | 2,079 | 156.8 | 3 | 0.1 |
| NF Salmon | 2,049 | 155.3 | 2,646 | 236.2 | 3 | 0.2 |
| Pahsimeroi | 1,924 | 75.0 | 2,481 | 168.1 | 2 | 0.1 |
| Panther Cr | 2,105 | 106.6 | 2,664 | 156.4 | 2 | 0.1 |
| Upper Salmon | 1,485 | 90.5 | 1,895 | 237.8 | 3 | 0.2 |
| Valley Cr | 1,465 | 89.1 | 2,401 | 260.7 | 3 | 0.2 |
| Yankee Fork | 1,249 | 78.1 | 2,154 | 243.4 | 3 | 0.2 |
Comparisons of watershed capacity estimates from the previous QRF and extrapolation model and the new revised versions reveal modest differences in most cases, with an exception of Chinook parr summer capacities in several watersheds. The substantial increases in Chinook parr summer capacity are likely due to the inclusion of elevation in the extrapolation model and range from 21 - 222% compared to the previous extrapolation.
Figure 5.1: Comparison of Chinook salmon habitat capacity estimates between revised and original model extrapolation, by life-stage, for the eight watersheds within the Upper Salmon River Basin.
| Model | Watershed | Capacity per km | Total capacity | Capacity % change | Capacity SE |
|---|---|---|---|---|---|
| Juv summer | EF Salmon | 12,335.5 | 1,926,623 | 112 | 226,926 |
| Juv summer | Lemhi | 5,765.9 | 786,452 | 112 | 62,660 |
| Juv summer | NF Salmon | 6,503.6 | 339,275 | 13 | 50,148 |
| Juv summer | Pahsimeroi | 5,145.6 | 265,099 | 45 | 18,409 |
| Juv summer | Panther Cr | 8,543.7 | 1,219,542 | 21 | 118,369 |
| Juv summer | Upper Salmon | 17,081.6 | 3,301,286 | 163 | 352,419 |
| Juv summer | Valley Cr | 15,832.8 | 1,902,198 | 152 | 207,363 |
| Juv summer | Yankee Fork | 14,967.3 | 2,144,056 | 222 | 274,556 |
| Juv winter | EF Salmon | 884.9 | 138,214 | 0 | 32,880 |
| Juv winter | Lemhi | 1,037.5 | 141,515 | -8 | 15,359 |
| Juv winter | NF Salmon | 1,350.7 | 70,462 | 28 | 10,409 |
| Juv winter | Pahsimeroi | 1,688.7 | 86,999 | -8 | 9,781 |
| Juv winter | Panther Cr | 1,410.0 | 201,265 | 29 | 22,296 |
| Juv winter | Upper Salmon | 861.6 | 166,522 | -29 | 45,582 |
| Juv winter | Valley Cr | 961.5 | 115,517 | -12 | 32,535 |
| Juv winter | Yankee Fork | 832.8 | 119,298 | 20 | 28,783 |
| Redds | EF Salmon | 2.6 | 402 | -13 | 21 |
| Redds | Lemhi | 2.6 | 353 | 5 | 11 |
| Redds | NF Salmon | 3.2 | 166 | -5 | 8 |
| Redds | Pahsimeroi | 2.7 | 139 | 25 | 4 |
| Redds | Panther Cr | 3.1 | 448 | -4 | 17 |
| Redds | Upper Salmon | 3.0 | 575 | -20 | 29 |
| Redds | Valley Cr | 3.3 | 394 | -29 | 20 |
| Redds | Yankee Fork | 3.1 | 438 | -38 | 23 |
Figure 5.2: Comparison of steelhead habitat capacity estimates between modified and original models extrapolation, by life-stage, for the eight watersheds within the Upper Salmon River Basin.
| Model | Watershed | Capacity per km | Total capacity | Capacity % change | Capacity SE |
|---|---|---|---|---|---|
| Juv summer | EF Salmon | 1,525.4 | 252,597 | -31 | 15,521 |
| Juv summer | Lemhi | 1,774.2 | 310,577 | -15 | 9,082 |
| Juv summer | NF Salmon | 2,048.7 | 242,471 | -5 | 18,382 |
| Juv summer | Pahsimeroi | 1,924.2 | 159,705 | -18 | 6,225 |
| Juv summer | Panther Cr | 2,105.3 | 268,476 | -8 | 13,598 |
| Juv summer | Upper Salmon | 1,484.6 | 243,548 | -31 | 14,844 |
| Juv summer | Valley Cr | 1,465.0 | 176,048 | -28 | 10,708 |
| Juv summer | Yankee Fork | 1,249.4 | 197,926 | -29 | 12,379 |
| Juv winter | EF Salmon | 2,039.2 | 337,682 | -14 | 36,795 |
| Juv winter | Lemhi | 2,078.7 | 363,898 | -8 | 27,441 |
| Juv winter | NF Salmon | 2,645.6 | 313,118 | -1 | 27,955 |
| Juv winter | Pahsimeroi | 2,481.0 | 205,921 | -4 | 13,951 |
| Juv winter | Panther Cr | 2,663.6 | 339,671 | 8 | 19,946 |
| Juv winter | Upper Salmon | 1,895.1 | 310,879 | -26 | 39,013 |
| Juv winter | Valley Cr | 2,401.4 | 288,579 | -14 | 31,329 |
| Juv winter | Yankee Fork | 2,154.4 | 341,310 | -18 | 38,555 |
| Redds | EF Salmon | 2.5 | 413 | -13 | 24 |
| Redds | Lemhi | 2.5 | 441 | 10 | 18 |
| Redds | NF Salmon | 2.7 | 323 | -10 | 22 |
| Redds | Pahsimeroi | 2.4 | 198 | 2 | 8 |
| Redds | Panther Cr | 2.5 | 317 | -7 | 15 |
| Redds | Upper Salmon | 2.8 | 452 | -11 | 32 |
| Redds | Valley Cr | 3.0 | 365 | -20 | 26 |
| Redds | Yankee Fork | 2.8 | 449 | -25 | 36 |
Figure 6.1: Partial dependence plots for covariates included in the juvenile summer QRF models
Figure 6.2: Partial dependence plots for covariates included in the juvenile summer QRF models
Figure 6.3: Partial dependence plots for covariates included in the juvenile winter QRF models
Figure 6.4: Partial dependence plots for covariates included in the juvenile winter QRF models
Figure 6.5: Partial dependence plots for covariates included in the QRF redds models
Figure 6.6: Partial dependence plots for covariates included in the QRF redds models